{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"}],"enrichment":{"capability":"MLX-Audio provides text-to-speech, speech-to-text, and speech-to-speech inference optimized for Apple Silicon, with support for multiple model architectures and multilingual capabilities.","skillfed_tags":["apple-silicon","speech-synthesis","mlx-framework"],"use_cases":["Generate speech from text on your Mac using multiple TTS model architectures with voice selection and language hints.","Build a local voice assistant or chatbot that speaks without sending audio to a cloud service.","Clone a voice by providing sample audio and then synthesize new speech in that voice across different models.","Stream audio output during generation for real-time playback or save multi-segment outputs as a single joined file.","Integrate speech synthesis into a macOS or iOS app via the Swift package or REST API.","Experiment with different quantized model variants (4-bit, 8-bit) to balance quality and inference speed on Apple Silicon."],"what_it_does":"MLX-Audio is a speech synthesis and processing library built on Apple's MLX framework, designed to run inference for text-to-speech, speech-to-text, and speech-to-speech tasks directly on Apple Silicon Macs. It wraps multiple pre-trained model architectures (Kokoro, Qwen3-TTS, OmniVoice, and others) with support for multilingual input, voice customization, and quantization options (3-bit through 8-bit). The package exposes both a command-line interface and a Python API, letting you generate audio, control speech speed, clone voices, and stream results during generation.\n\nThe library depends on huggingface_hub for model downloads, mlx and mlx-lm for inference, numpy and scipy for audio processing, transformers for tokenization, sounddevice for playback, miniaudio for audio I/O, and tqdm for progress reporting. It targets developers building voice applications on macOS who want local, on-device inference without cloud dependencies.","worth_installing":"Yes, if you have Apple Silicon and need local TTS/STS inference. Active maintenance, permissive license, low install friction, no known vulnerabilities, and a large model ecosystem make it a solid choice. Verify whether STT is production-ready and whether the web interface requires optional extras before committing to a deployment."},"id":"mlx-audio","links":{"html":"https://skillfed.io/packages/mlx-audio","md":"https://skillfed.io/packages/mlx-audio.md","pypi":"https://pypi.org/project/mlx-audio/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-10","license_spdx":"MIT","license_treatment":"permissive","name":"mlx-audio","python_support":"supports_current","summary":"MLX-Audio is a package for inference of text-to-speech (TTS) and speech-to-speech (STS) models locally on your Mac using MLX"},"popularity":{"monthly_downloads":394354,"position":6993,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.4.8"}